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<div id="section-journal-based" class="section level2">
<h2>Journal based</h2>
<div id="section-usage" class="section level3">
<h3>Usage</h3>
<p>Change the journal ISSN number before [TA] and the time range before[DP] to get the records from pubmed. I limited the numbers of records to 10000. Since such fetch from Pubmed might be blocked, try to use it carefully. I suggest every query should be limited no more than 8000, which is enough to show some trends.</p>
<p>Just click <code>Run Code</code> to get the results</p>
<div class="tutorial-exercise" data-label="journal" data-caption="Code" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>query &lt;- &#39;0003-2700[TA] AND 2017/06:2017/08[DP]&#39;
# fetch data
tmdf &lt;- getpubmed(query, start = 1, end = 10000) %&gt;%
        getpubmedtbl() %&gt;%
        mutate(time = as.POSIXct(date, origin = &quot;1970-01-01&quot;),
         month = round_date(date, &quot;month&quot;))
# get words in title
wordft &lt;- tmdf %&gt;%
        filter(nchar(title) &gt; 0) %&gt;%
        unnest_tokens(word, title,drop = F) %&gt;%
        anti_join(stop_words) %&gt;%
        filter(str_detect(word, &quot;[^\\d]&quot;)) %&gt;%
        group_by(word) %&gt;%
        mutate(word_total = n()) %&gt;%
        ungroup() %&gt;%
        mutate(source = &#39;title&#39;)
# plot top 20 words in title
wordft %&gt;%
        count(word, sort = TRUE) %&gt;%
        top_n(20,n) %&gt;%
        mutate(word = reorder(word, n)) %&gt;%
        ggplot(aes(word, n)) +
  geom_col(show.legend = FALSE) +
  ylab(&quot;Top 20 commonly used words in titles&quot;) +
  coord_flip()
# get words in abstracts
wordfabs &lt;- tmdf %&gt;%
        filter(nchar(abstract) &gt; 0) %&gt;%
        unnest_tokens(word, abstract,drop = F) %&gt;%
        anti_join(stop_words) %&gt;%
        filter(str_detect(word, &quot;[^\\d]&quot;)) %&gt;%
        group_by(word) %&gt;%
        mutate(word_total = n()) %&gt;%
        ungroup() %&gt;%
        mutate(source = &#39;abstract&#39;)
        
# plot top 20 words in abstracts
wordfabs %&gt;%
        count(word, sort = TRUE) %&gt;%
        top_n(20,n) %&gt;%
        mutate(word = reorder(word, n)) %&gt;%
        ggplot(aes(word, n)) +
  geom_col(show.legend = FALSE) +
  ylab(&quot;Top 20 commonly used words in abstracts&quot;) +
  coord_flip()
        
        
# Temporal Trends
papers_per_month &lt;- tmdf %&gt;%
  group_by(month) %&gt;%
  summarize(month_total = n())
# Growing words in titles
word_month_counts &lt;- wordft %&gt;%
  filter(word_total &gt;= 100) %&gt;%
  count(word, month) %&gt;%
  complete(word, month, fill = list(n = 0)) %&gt;%
  inner_join(papers_per_month, by = &quot;month&quot;) %&gt;%
  mutate(percent = n / month_total) %&gt;%
  mutate(year = year(month) + yday(month) / 365) %&gt;%
        filter(percent &lt; 0.8)

mod &lt;- ~ glm(cbind(n, month_total - n) ~ year, ., family = &quot;binomial&quot;)

slopes &lt;- word_month_counts %&gt;%
  nest(-word) %&gt;%
  mutate(model = map(data, mod)) %&gt;%
  unnest(map(model, tidy)) %&gt;%
  filter(term == &quot;year&quot;) %&gt;%
  arrange(desc(estimate))

slopes %&gt;%
  head(9) %&gt;%
  inner_join(word_month_counts, by = &quot;word&quot;) %&gt;%
  mutate(word = reorder(word, -estimate)) %&gt;%
  ggplot(aes(month, n / month_total, color = word)) +
  geom_line(show.legend = FALSE) +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~ word, scales = &quot;free_y&quot;) +
  expand_limits(y = 0) +
  labs(x = &quot;Year&quot;,
       y = &quot;Percentage of titles containing this term&quot;,
       title = &quot;9 fastest growing words&quot;,
       subtitle = &quot;Judged by growth rate&quot;
              )
# shrinking words in titles
slopes %&gt;%
  tail(9) %&gt;%
  inner_join(word_month_counts, by = &quot;word&quot;) %&gt;%
  mutate(word = reorder(word, estimate)) %&gt;%
  ggplot(aes(month, n / month_total, color = word)) +
  geom_line(show.legend = FALSE) +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~ word, scales = &quot;free_y&quot;) +
  expand_limits(y = 0) +
  labs(x = &quot;Year&quot;,
       y = &quot;Percentage of titles containing this term&quot;,
       title = &quot;9 fastest shrinking words&quot;,
       subtitle = &quot;Judged by growth rate&quot;
              )

# Growing words in abstracts
word_month_counts &lt;- wordfabs %&gt;%
  filter(word_total &gt;= 100) %&gt;%
  count(word, month) %&gt;%
  complete(word, month, fill = list(n = 0)) %&gt;%
  inner_join(papers_per_month, by = &quot;month&quot;) %&gt;%
  mutate(percent = n / month_total) %&gt;%
  mutate(year = year(month) + yday(month) / 365) %&gt;%
        filter(percent &lt; 0.8)

mod &lt;- ~ glm(cbind(n, month_total - n) ~ year, ., family = &quot;binomial&quot;)

slopes &lt;- word_month_counts %&gt;%
  nest(-word) %&gt;%
  mutate(model = map(data, mod)) %&gt;%
  unnest(map(model, tidy)) %&gt;%
  filter(term == &quot;year&quot;) %&gt;%
  arrange(desc(estimate))

slopes %&gt;%
  head(9) %&gt;%
  inner_join(word_month_counts, by = &quot;word&quot;) %&gt;%
  mutate(word = reorder(word, -estimate)) %&gt;%
  ggplot(aes(month, n / month_total, color = word)) +
  geom_line(show.legend = FALSE) +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~ word, scales = &quot;free_y&quot;) +
  expand_limits(y = 0) +
  labs(x = &quot;Year&quot;,
       y = &quot;Percentage of abstracts containing this term&quot;,
       title = &quot;9 fastest growing words&quot;,
       subtitle = &quot;Judged by growth rate&quot;)
        
# shrinking words in abstracts
slopes %&gt;%
  tail(9) %&gt;%
  inner_join(word_month_counts, by = &quot;word&quot;) %&gt;%
  mutate(word = reorder(word, estimate)) %&gt;%
  ggplot(aes(month, n / month_total, color = word)) +
  geom_line(show.legend = FALSE) +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~ word, scales = &quot;free_y&quot;) +
  expand_limits(y = 0) +
  labs(x = &quot;Year&quot;,
       y = &quot;Percentage of abstracts containing this term&quot;,
       title = &quot;9 fastest shrinking words&quot;,
       subtitle = &quot;Judged by growth rate&quot;)

# topic model
desc_dtm &lt;- wordfabs %&gt;%
        count(line, word, sort = TRUE) %&gt;%
        ungroup() %&gt;%
        cast_dtm(line, word, n)

desc_lda &lt;- LDA(desc_dtm, k = 5, control = list(seed = 42))
tidy_lda &lt;- tidy(desc_lda)

top_terms &lt;- tidy_lda %&gt;%
  group_by(topic) %&gt;%
  top_n(10, beta) %&gt;%
  ungroup() %&gt;%
  arrange(topic, -beta)

top_terms %&gt;%
  mutate(term = reorder(term, beta)) %&gt;%
  group_by(topic, term) %&gt;%    
  arrange(desc(beta)) %&gt;%  
  ungroup() %&gt;%
  mutate(term = factor(paste(term, topic, sep = &quot;__&quot;), 
                       levels = rev(paste(term, topic, sep = &quot;__&quot;)))) %&gt;%
  ggplot(aes(term, beta, fill = as.factor(topic))) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  scale_x_discrete(labels = function(x) gsub(&quot;__.+$&quot;, &quot;&quot;, x)) +
  labs(title = &quot;Top 10 terms in each LDA topic&quot;,
       x = NULL, y = expression(beta)) +
  facet_wrap(~ topic, ncol = 5, scales = &quot;free&quot;)</code></pre>
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</div>
</div>
</div>
<div id="section-keywords-based" class="section level2">
<h2>Keywords based</h2>
<div id="section-usage-1" class="section level3">
<h3>Usage</h3>
<p>Change the keywords before [MH] and the time range before[DP] to get the records from pubmed. I limited the numbers of records to 10000. Since such fetch from Pubmed might be blocked, try to use it carefully. I suggest every query should be limited no more than 8000, which is enough to show some trends.</p>
<p>Just click <code>Run Code</code> to get the results</p>
<div class="tutorial-exercise" data-label="keywords" data-caption="Code" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>query &lt;- &#39;Solid Phase MicroExtraction[MH] AND 1997/08:2017/08[DP]&#39;
tmdf &lt;- getpubmed(query, start = 1, end = 10000) %&gt;%
        getpubmedtbl() %&gt;%
        mutate(time = as.POSIXct(date, origin = &quot;1970-01-01&quot;),
         month = round_date(date, &quot;month&quot;))
# Numbers by journal
tmdf %&gt;%
  group_by(journal) %&gt;%
  summarize(papers = n_distinct(title)) %&gt;%
  top_n(20,papers) %&gt;%
  mutate(journal = reorder(journal, papers)) %&gt;%     
  ggplot(aes(journal, papers)) +
  geom_col() +
  coord_flip()
# Get the words in title (journal with papers &gt; 100)
wordft &lt;- tmdf %&gt;%
        filter(nchar(title) &gt; 0) %&gt;%
        group_by(journal) %&gt;%
        mutate(journal_total = n()) %&gt;%
        ungroup() %&gt;%
        filter(journal_total &gt; 100) %&gt;%
        unnest_tokens(word, title,drop = F) %&gt;%
        anti_join(stop_words) %&gt;%
        filter(str_detect(word, &quot;[^\\d]&quot;)) %&gt;%
        group_by(word) %&gt;%
        mutate(word_total = n()) %&gt;%
        ungroup() %&gt;%
        mutate(source = &#39;title&#39;)
  
# get words in abstracts
wordfabs &lt;- tmdf %&gt;%
        filter(nchar(abstract) &gt; 0) %&gt;%
        group_by(journal) %&gt;%
        mutate(journal_total = n()) %&gt;%
        ungroup() %&gt;%
        filter(journal_total &gt; 100) %&gt;%
        unnest_tokens(word, abstract,drop = F) %&gt;%
        anti_join(stop_words) %&gt;%
        filter(str_detect(word, &quot;[^\\d]&quot;)) %&gt;%
        group_by(word) %&gt;%
        mutate(word_total = n()) %&gt;%
        ungroup() %&gt;%
        mutate(source = &#39;abstract&#39;)
        
# plot top 20 words in abstracts
wordfabs %&gt;%
        count(word, sort = TRUE) %&gt;%
        top_n(20,n) %&gt;%
        mutate(word = reorder(word, n)) %&gt;%
        ggplot(aes(word, n)) +
  geom_col(show.legend = FALSE) +
  ylab(&quot;Top 20 commonly used words in abstracts&quot;) +
  coord_flip()
        
# Temporal Trends
papers_per_month &lt;- tmdf %&gt;%
  group_by(month) %&gt;%
  summarize(month_total = n())
# Growing words in titles
word_month_counts &lt;- wordft %&gt;%
  filter(word_total &gt;= 100) %&gt;%
  count(word, month) %&gt;%
  complete(word, month, fill = list(n = 0)) %&gt;%
  inner_join(papers_per_month, by = &quot;month&quot;) %&gt;%
  mutate(percent = n / month_total) %&gt;%
  mutate(year = year(month) + yday(month) / 365) %&gt;%
        filter(percent &lt; 0.8)

mod &lt;- ~ glm(cbind(n, month_total - n) ~ year, ., family = &quot;binomial&quot;)

slopes &lt;- word_month_counts %&gt;%
  nest(-word) %&gt;%
  mutate(model = map(data, mod)) %&gt;%
  unnest(map(model, tidy)) %&gt;%
  filter(term == &quot;year&quot;) %&gt;%
  arrange(desc(estimate))

slopes %&gt;%
  head(9) %&gt;%
  inner_join(word_month_counts, by = &quot;word&quot;) %&gt;%
  mutate(word = reorder(word, -estimate)) %&gt;%
  ggplot(aes(month, n / month_total, color = word)) +
  geom_line(show.legend = FALSE) +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~ word, scales = &quot;free_y&quot;) +
  expand_limits(y = 0) +
  labs(x = &quot;Year&quot;,
       y = &quot;Percentage of titles containing this term&quot;,
       title = &quot;9 fastest growing words&quot;,
       subtitle = &quot;Judged by growth rate&quot;
              )
# shrinking words in titles
slopes %&gt;%
  tail(9) %&gt;%
  inner_join(word_month_counts, by = &quot;word&quot;) %&gt;%
  mutate(word = reorder(word, estimate)) %&gt;%
  ggplot(aes(month, n / month_total, color = word)) +
  geom_line(show.legend = FALSE) +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~ word, scales = &quot;free_y&quot;) +
  expand_limits(y = 0) +
  labs(x = &quot;Year&quot;,
       y = &quot;Percentage of titles containing this term&quot;,
       title = &quot;9 fastest shrinking words&quot;,
       subtitle = &quot;Judged by growth rate&quot;
              )

# Growing words in abstracts
word_month_counts &lt;- wordfabs %&gt;%
  filter(word_total &gt;= 100) %&gt;%
  count(word, month) %&gt;%
  complete(word, month, fill = list(n = 0)) %&gt;%
  inner_join(papers_per_month, by = &quot;month&quot;) %&gt;%
  mutate(percent = n / month_total) %&gt;%
  mutate(year = year(month) + yday(month) / 365) %&gt;%
        filter(percent &lt; 0.8)

mod &lt;- ~ glm(cbind(n, month_total - n) ~ year, ., family = &quot;binomial&quot;)

slopes &lt;- word_month_counts %&gt;%
  nest(-word) %&gt;%
  mutate(model = map(data, mod)) %&gt;%
  unnest(map(model, tidy)) %&gt;%
  filter(term == &quot;year&quot;) %&gt;%
  arrange(desc(estimate))

slopes %&gt;%
  head(9) %&gt;%
  inner_join(word_month_counts, by = &quot;word&quot;) %&gt;%
  mutate(word = reorder(word, -estimate)) %&gt;%
  ggplot(aes(month, n / month_total, color = word)) +
  geom_line(show.legend = FALSE) +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~ word, scales = &quot;free_y&quot;) +
  expand_limits(y = 0) +
  labs(x = &quot;Year&quot;,
       y = &quot;Percentage of abstracts containing this term&quot;,
       title = &quot;9 fastest growing words &quot;,
       subtitle = &quot;Judged by growth rate&quot;)
        
# shrinking words in abstracts
slopes %&gt;%
  tail(9) %&gt;%
  inner_join(word_month_counts, by = &quot;word&quot;) %&gt;%
  mutate(word = reorder(word, estimate)) %&gt;%
  ggplot(aes(month, n / month_total, color = word)) +
  geom_line(show.legend = FALSE) +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~ word, scales = &quot;free_y&quot;) +
  expand_limits(y = 0) +
  labs(x = &quot;Year&quot;,
       y = &quot;Percentage of abstracts containing this term&quot;,
       title = &quot;9 fastest shrinking words&quot;,
       subtitle = &quot;Judged by growth rate&quot;)

# topic model
desc_dtm &lt;- wordfabs %&gt;%
        count(line, word, sort = TRUE) %&gt;%
        ungroup() %&gt;%
        cast_dtm(line, word, n)

desc_lda &lt;- LDA(desc_dtm, k = 5, control = list(seed = 42))
tidy_lda &lt;- tidy(desc_lda)

top_terms &lt;- tidy_lda %&gt;%
  group_by(topic) %&gt;%
  top_n(10, beta) %&gt;%
  ungroup() %&gt;%
  arrange(topic, -beta)

top_terms %&gt;%
  mutate(term = reorder(term, beta)) %&gt;%
  group_by(topic, term) %&gt;%    
  arrange(desc(beta)) %&gt;%  
  ungroup() %&gt;%
  mutate(term = factor(paste(term, topic, sep = &quot;__&quot;), 
                       levels = rev(paste(term, topic, sep = &quot;__&quot;)))) %&gt;%
  ggplot(aes(term, beta, fill = as.factor(topic))) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  scale_x_discrete(labels = function(x) gsub(&quot;__.+$&quot;, &quot;&quot;, x)) +
  labs(title = &quot;Top 10 terms in each LDA topic&quot;,
       x = NULL, y = expression(beta)) +
  facet_wrap(~ topic, ncol = 5, scales = &quot;free&quot;) 

# Journal accent

words_by_journal &lt;- wordfabs %&gt;%
  count(journal, word, sort = TRUE) %&gt;%
  ungroup()

tf_idf &lt;- words_by_journal %&gt;%
  bind_tf_idf(word, journal, n) %&gt;%
  arrange(desc(tf_idf))

tf_idf %&gt;%
  group_by(journal) %&gt;%
  top_n(10, tf_idf) %&gt;%
  ungroup() %&gt;%
  mutate(word = reorder(word, tf_idf)) %&gt;%
  ggplot(aes(word, tf_idf, fill = journal)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ journal, scales = &quot;free&quot;) +
  ylab(&quot;tf-idf in abstracts&quot;) +
  coord_flip()</code></pre>
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